Some prior refrigeration systems entail the use of neural networks to control the duty cycle of the compressor on a consumer refrigerator-freezer unit. Neural networks and closely related support vector machines attempt to predict when and for how long the unit doors will be opened on the basis of past use. However, these older methods are associated with the following disadvantages at a minimum. First, the accuracy of such predictions is curtailed by the limited use of sensory data. Second, a symbolic model is not evolved, which third renders learning by these older methods NP-hard. Thus, they take an inordinate amount of time to adapt to changes in usage patterns and this is associated with the need for higher-end computer chips, which of course draw more electrical power—negating that which they are trying to save.